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Using SHAP Library for my LSTM model - "Attribute Error" - vatsalmtailor - Jul-13-2022
We used Keras to build our LSTM model as follows:
import keras from keras.models import Sequential from keras.layers import Dense from keras.layers import LSTM #make LSTM model architecture model2 = Sequential() model2.add(LSTM(100, return_sequences = True)) model2.add(LSTM(50, return_sequences = True)) model2.add(LSTM(10)) model2.add(Dense(1)) model2.compile(loss='mae', optimizer='adam')The model builds, trains and predicts successfully but we face the following error when we use SHAP on the LSTM model:
"Attribute Error": 'Deep' object has no attribute 'masker'
The following is how we tried to use SHAP:
import shap explainer = shap.DeepExplainer(model2,x_train_appended) shap_values = explainer(x_train_appended)
And the following is the error received:
Output:In [56]: import shap
...: explainer = shap.DeepExplainer(model2, x_train_appended)
...: shap_values = explainer(x_train_appended)
WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'list'> input: [<tf.Tensor: shape=(49586, 1, 23), dtype=float32, numpy=
array([[[0.40824828, 0.02564103, 0.03370786, ..., 0.4494382 ,
0.43333334, 0.59210527]],
[[0. , 0.06410257, 0.05617978, ..., 0.4494382 ,
0.43333334, 0.59210527]],
[[0.5400617 , 0.06410257, 0.06741573, ..., 0.4494382 ,
0.43333334, 0.59210527]],
...,
[[0.5400617 , 0.01282051, 0.05617978, ..., 0.07865169,
0.01111111, 0.05263158]],
[[0. , 0.02564103, 0.05617978, ..., 0.07865169,
0.01111111, 0.05263158]],
[[0. , 0.02564103, 0.05617978, ..., 0.07865169,
0.01111111, 0.05263158]]], dtype=float32)>]
Consider rewriting this model with the Functional API.
Traceback (most recent call last):
File "", line 3, in
shap_values = explainer(x_train_appended)
File "/home/kiton/.local/lib/python3.8/site-packages/shap/explainers/_explainer.py", line 207, in call
if issubclass(type(self.masker), maskers.OutputComposite) and len(args)==2:
AttributeError: 'Deep' object has no attribute 'masker'